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Ba Mahel AS, Cao S, Zhang K, Chelloug SA, Alnashwan R, Muthanna MSA. Advanced integration of 2DCNN-GRU model for accurate identification of shockable life-threatening cardiac arrhythmias: a deep learning approach. Front Physiol 2024; 15:1429161. [PMID: 39072217 PMCID: PMC11272599 DOI: 10.3389/fphys.2024.1429161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 06/17/2024] [Indexed: 07/30/2024] Open
Abstract
Cardiovascular diseases remain one of the main threats to human health, significantly affecting the quality and life expectancy. Effective and prompt recognition of these diseases is crucial. This research aims to develop an effective novel hybrid method for automatically detecting dangerous arrhythmias based on cardiac patients' short electrocardiogram (ECG) fragments. This study suggests using a continuous wavelet transform (CWT) to convert ECG signals into images (scalograms) and examining the task of categorizing short 2-s segments of ECG signals into four groups of dangerous arrhythmias that are shockable, including ventricular flutter (C1), ventricular fibrillation (C2), ventricular tachycardia torsade de pointes (C3), and high-rate ventricular tachycardia (C4). We propose developing a novel hybrid neural network with a deep learning architecture to classify dangerous arrhythmias. This work utilizes actual electrocardiogram (ECG) data obtained from the PhysioNet database, alongside artificially generated ECG data produced by the Synthetic Minority Over-sampling Technique (SMOTE) approach, to address the issue of imbalanced class distribution for obtaining an accuracy-trained model. Experimental results demonstrate that the proposed approach achieves high accuracy, sensitivity, specificity, precision, and an F1-score of 97.75%, 97.75%, 99.25%, 97.75%, and 97.75%, respectively, in classifying all the four shockable classes of arrhythmias and are superior to traditional methods. Our work possesses significant clinical value in real-life scenarios since it has the potential to significantly enhance the diagnosis and treatment of life-threatening arrhythmias in individuals with cardiac disease. Furthermore, our model also has demonstrated adaptability and generality for two other datasets.
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Affiliation(s)
- Abduljabbar S. Ba Mahel
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shenghong Cao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Kaixuan Zhang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Samia Allaoua Chelloug
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Rana Alnashwan
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
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2
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Prusty MR, Pandey TN, Lekha PS, Lellapalli G, Gupta A. Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals. Sci Rep 2024; 14:2633. [PMID: 38302520 PMCID: PMC10834984 DOI: 10.1038/s41598-024-53107-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/27/2024] [Indexed: 02/03/2024] Open
Abstract
Heart diseases are leading to death across the globe. Exact detection and treatment for heart disease in its early stages could potentially save lives. Electrocardiogram (ECG) is one of the tests that take measures of heartbeat fluctuations. The deviation in the signals from the normal sinus rhythm and different variations can help detect various heart conditions. This paper presents a novel approach to cardiac disease detection using an automated Convolutional Neural Network (CNN) system. Leveraging the Scale-Invariant Feature Transform (SIFT) for unique ECG signal image feature extraction, our model classifies signals into three categories: Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). The proposed model has been evaluated using 96 Arrhythmia, 30 CHF, and 36 NSR ECG signals, resulting in a total of 162 images for classification. Our proposed model achieved 99.78% accuracy and an F1 score of 99.78%, which is among one of the highest in the models which were recorded to date with this dataset. Along with the SIFT, we also used HOG and SURF techniques individually and applied the CNN model which achieved 99.45% and 78% accuracy respectively which proved that the SIFT-CNN model is a well-trained and performed model. Notably, our approach introduces significant novelty by combining SIFT with a custom CNN model, enhancing classification accuracy and offering a fresh perspective on cardiac arrhythmia detection. This SIFT-CNN model performed exceptionally well and better than all existing models which are used to classify heart diseases.
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Affiliation(s)
- Manas Ranjan Prusty
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India
| | - Trilok Nath Pandey
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India.
| | - Pujala Shree Lekha
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India
| | - Gayatri Lellapalli
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India
| | - Annika Gupta
- School of Electrical Engineering, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India
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3
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Laghari AA, Sun Y, Alhussein M, Aurangzeb K, Anwar MS, Rashid M. Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection. Sci Rep 2023; 13:15109. [PMID: 37704659 PMCID: PMC10499947 DOI: 10.1038/s41598-023-40343-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 08/09/2023] [Indexed: 09/15/2023] Open
Abstract
Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will seriously harm the life and health of patients. Traditional deep learning methods have weak anti-interference and generalization ability. Therefore, we propose a new-fashioned deep residual-dense network via bidirectional recurrent neural network (RNN) model for atrial fibrillation detection. The combination of one-dimensional dense residual network and bidirectional RNN for atrial fibrillation detection simplifies the tedious feature extraction steps, and constructs the end-to-end neural network to achieve atrial fibrillation detection through data feature learning. Meanwhile, the attention mechanism is utilized to fuse the different features and extract the high-value information. The accuracy of the experimental results is 97.72%, the sensitivity and specificity are 93.09% and 98.71%, respectively compared with other methods.
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Affiliation(s)
- Asif Ali Laghari
- Software College, Shenyang Normal University, Shenyang, 110034, China
| | - Yanqiu Sun
- Liaoning University of Traditional Chinese Medicine, Shenyang, China.
| | - Musaed Alhussein
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Kingdom of Saudi Arabia
| | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Kingdom of Saudi Arabia
| | | | - Mamoon Rashid
- Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, 411048, India
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4
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Ayad A, Barhoush M, Frei M, Volker B, Schmeink A. An Efficient and Private ECG Classification System Using Split and Semi-Supervised Learning. IEEE J Biomed Health Inform 2023; 27:4261-4272. [PMID: 37262112 DOI: 10.1109/jbhi.2023.3281977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG recordings using deep neural networks has been investigated in literature and has shown very good performance. However, this performance assumes that the training data is centralized, which is often not the case in real-life scenarios, where data resides in multiple places and only a small portion of it is labeled. Therefore, in this work, we propose an ECG classification system that focuses on preserving data privacy and enhancing overall system efficiency. We analyzed the complexity of previously proposed deep learning-based models and showed that the temporal convolutional network-based models (TCN) were the most efficient. Then, we built on the TCN models a modified split-learning (SL) system that achieves the same classification performance as the basic SL but reduces the communication overhead between the server and the client by 71.7% as well as reducing the computations at the client by 46.5% compared to the original SL system based on the TCN network. Finally, we implement semi-supervised learning in our system to enhance its classification performance by 9.1%-15.7%, when the training data consists only of 10% labeled data. We have tested our proposed system on a test IoT setup and it achieved satisfactory classification accuracy while being private and energy efficient for green-AI applications.
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5
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Boda S, Mahadevappa M, Kumar Dutta P. An automated patient-specific ECG beat classification using LSTM-based recurrent neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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6
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Martínez Coq P, Rey A, Rosso OA, Armentano R, Legnani W. Detection of cardiac arrhythmia patterns in ECG through H × C plane. CHAOS (WOODBURY, N.Y.) 2022; 32:123118. [PMID: 36587353 DOI: 10.1063/5.0118717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/10/2022] [Indexed: 06/17/2023]
Abstract
The aim of this study is to formulate a new methodology based upon informational tools to detect patients with cardiac arrhythmias. As it is known, sudden death is the consequence of a final arrhythmia, and here lies the relevance of the efforts aimed at the early detection of arrhythmias. The information content in the time series from an electrocardiogram (ECG) signal is conveyed in the form of a probability distribution function, to compute the permutation entropy proposed by Bandt and Pompe. This selection was made seeking its remarkable conceptual simplicity, computational speed, and robustness to noise. In this work, two well-known databases were used, one containing normal sinus rhythms and another one containing arrhythmias, both from the MIT medical databank. For different values of embedding time delay τ, normalized permutation entropy and statistical complexity measure are computed to finally represent them on the horizontal and vertical axes, respectively, which define the causal plane H×C. To improve the results obtained in previous works, a feature set composed by these two magnitudes is built to train the following supervised machine learning algorithms: random forest (RF), support vector machine (SVM), and k nearest neighbors (kNN). To evaluate the performance of each classification technique, a 10-fold cross-validation scheme repeated 10 times was implemented. Finally, to select the best model, three quality parameters were computed, namely, accuracy, the area under the receiver operative characteristic (ROC) curve (AUC), and the F1-score. The results obtained show that the best classification model to detect the ECG coming from arrhythmic patients is RF. The values of the quality parameters were at the same levels reported in the available literature using a larger data set, thus supporting this proposal that uses a very small-sized feature space to train the model later used to classify. Summarizing, the attained results show the possibility to discriminate both groups of patients, with normal sinus rhythm or arrhythmic ECG, showing a promising efficiency in the definition of new markers for the detection of cardiovascular pathologies.
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Affiliation(s)
- P Martínez Coq
- Signal and Image Processing Center (CPSI), Facultad Regional Buenos Aires. Universidad Tecnológica Nacional, Ciudad Autónoma de Buenos Aires C1179AAQ, Argentina
| | - A Rey
- Signal and Image Processing Center (CPSI), Facultad Regional Buenos Aires. Universidad Tecnológica Nacional, Ciudad Autónoma de Buenos Aires C1179AAQ, Argentina
| | - O A Rosso
- Physics Institute, Universidade Federal de Alagoas (UFAL), Maceió CEP 57072-900, Brazil
| | - R Armentano
- Bioengineering Research and Development Group (GIBIO), Facultad Regional Buenos Aires. Universidad Tecnológica Nacional, Ciudad Autónoma de Buenos Aires C1179AAQ, Argentina
| | - W Legnani
- Signal and Image Processing Center (CPSI), Facultad Regional Buenos Aires. Universidad Tecnológica Nacional, Ciudad Autónoma de Buenos Aires C1179AAQ, Argentina
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7
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Diagnostic AI and Cardiac Diseases. Diagnostics (Basel) 2022; 12:diagnostics12122901. [PMID: 36552908 PMCID: PMC9776503 DOI: 10.3390/diagnostics12122901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022] Open
Abstract
(1) Background: The purpose of this study is to review and highlight recent advances in diagnostic uses of artificial intelligence (AI) for cardiac diseases, in order to emphasize expected benefits to both patients and healthcare specialists; (2) Methods: We focused on four key search terms (Cardiac Disease, diagnosis, artificial intelligence, machine learning) across three different databases (Pubmed, European Heart Journal, Science Direct) between 2017-2022 in order to reach relatively more recent developments in the field. Our review was structured in order to clearly differentiate publications according to the disease they aim to diagnose (coronary artery disease, electrophysiological and structural heart diseases); (3) Results: Each study had different levels of success, where declared sensitivity, specificity, precision, accuracy, area under curve and F1 scores were reported for every article reviewed; (4) Conclusions: the number and quality of AI-assisted cardiac disease diagnosis publications will continue to increase through each year. We believe AI-based diagnosis should only be viewed as an additional tool assisting doctors' own judgement, where the end goal is to provide better quality of healthcare and to make getting medical help more affordable and more accessible, for everyone, everywhere.
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8
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Marzog HA, Abd HJ. Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022; 2022:1-8. [DOI: 10.1155/2022/9884076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023] Open
Abstract
The heart’s electrical activity is registered by an electrocardiogram (ECG), which consists of a wealth of pathological data on heart diseases such as arrhythmia. However, with increasing complexity and nonlinearity, direct observation of ECG signals and analysis is very tough. The highest accuracy of classification performance for machine learning approaches are 99.7 for neural network with wavelet scattering features extraction and 99.92 for SVM also with wavelet scattering features extraction. Through wavelet cascades with a neural network, the wavelet scattering transform can yield a translation invariant and deflection depictions of ECG signals. We suggested a new wavelet scattering transform-based method for automatically classifying three types of ECG heart diseases as follows: arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The bandwidth of the scaling function is used to critically downsample the wavelet scattering transform in time. As a result, each of the scattering paths has 16-time windows. Beat classification performance is classified by utilizing the MIT-BIH arrhythmia dataset. The suggested method is able to conduct high accuracy arrhythmia classification, with a 99.7% and 99.92% accuracy rate of the neural network (NN) and support vector machine (SVM), respectively, and will aid physicians in ECG explanation.
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Affiliation(s)
- Heyam A. Marzog
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
- Engineering Technical College/Najaf, Al-Furat Al-Awsat Technical University, Al Najaf 31001, Iraq
| | - Haider. J. Abd
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
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9
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A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection. J Clin Med 2022; 11:jcm11174935. [PMID: 36078865 PMCID: PMC9456488 DOI: 10.3390/jcm11174935] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/17/2022] Open
Abstract
An automatic electrocardiogram (ECG) myocardial infarction detection system needs to satisfy several requirements to be efficient in real-world practice. These requirements, such as reliability, less complexity, and high performance in decision-making, remain very important in a realistic clinical environment. In this study, we investigated an automatic ECG myocardial infarction detection system and presented a new approach to evaluate its robustness and durability performance in classifying the myocardial infarction (with no feature extraction) under different noise types. We employed three well-known supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF), and tested the performance and robustness of these techniques in classifying normal (NOR) and myocardial infarction (MI) using real ECG records from the PTB database after normalization and segmentation of the data, with a suggested inter-patient paradigm separation as well as noise from the MIT-BIH noise stress test database (NSTDB). Finally, we measured four metrics: accuracy, precision, recall, and F1-score. The simulation revealed that all of the models performed well, with values of over 0.50 at lower SNR levels, in terms of all the metrics investigated against different types of noise, indicating that they are encouraging and acceptable under extreme noise situations are are thus considered sustainable and robust models for specific forms of noise. All of the methods tested could be used as ECG myocardial infarction detection tools in real-world practice under challenging circumstances.
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10
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Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8413294. [PMID: 35978890 PMCID: PMC9377844 DOI: 10.1155/2022/8413294] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/05/2022] [Accepted: 07/19/2022] [Indexed: 11/18/2022]
Abstract
The electrical activity produced during the heartbeat is measured and recorded by an ECG. Cardiologists can interpret the ECG machine’s signals and determine the heart’s health condition and related causes of ECG signal abnormalities. However, cardiologist shortage is a challenge in both developing and developed countries. Moreover, the experience of a cardiologist matters in the accurate interpretation of the ECG signal, as the interpretation of ECG is quite tricky even for experienced doctors. Therefore, developing computer-aided ECG interpretation is required for its wide-reaching effect. 12-lead ECG generates a 1D signal with 12 channels among the well-known time-series data. Classical machine learning can develop automatic detection, but deep learning is more effective in the classification task. 1D-CNN is being widely used for CVDS detection from ECG datasets. However, adopting a deep learning model designed for computer vision can be problematic because of its massive parameters and the need for many samples to train. In many detection tasks ranging from semantic segmentation of medical images to time-series data classification, multireceptive field CNN has improved performance. Notably, the nature of the ECG dataset made performance improvement possible by using a multireceptive field CNN (MRF-CNN). Using MRF-CNN, it is possible to design a model that considers semantic context information within ECG signals with different sizes. As a result, this study has designed a multireceptive field CNN architecture for ECG classification. The proposed multireceptive field CNN architecture can improve the performance of ECG signal classification. We have achieved a 0.72
score and 0.93 AUC for 5 superclasses, a 0.46
score and 0.92 AUC for 20 subclasses, and a 0.31
score and 0.92 AUC for all the diagnostic classes of the PTB-XL dataset.
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11
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Pandey SK, Janghel RR, Mishra PK, Kaabra R. Machine learning based COVID -19 disease recognition using CT images of SIRM database. J Med Eng Technol 2022; 46:590-603. [PMID: 35639099 DOI: 10.1080/03091902.2022.2080883] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The COVID-19 pandemic, probably one of the most widespread pandemics humanity has encountered in the twenty first century, caused death to almost 1.75 M people worldwide, impacting almost 80 M lives with direct contact. In order to contain the spread of coronavirus, it is necessary to develop a reliant and quick method to identify those who are affected and isolate them until full recovery is made. The imagery knowledge has been shown to be useful for quick COVID-19 diagnosis. Though the scans of computational tomography (CT) demonstrate a range of viral infection signals, considering the vast number of images, certain visual characteristics are challenging to distinguish and can take a long time to be identified by radiologists. In this study for detection of the COVID-19, a dataset is formed by taking 3764 images. The feature extraction process is applied to the dataset to increase the classification performance. Techniques like Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used for feature extraction. Then various machine learning algorithms applied such as Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Multi- Level Perceptron, Naive Bayes, K-Nearest Neighbours and Random Forests are used for classification of COVID-19 disease detection. Sensitivity, Specificity, Accuracy, Precision, and F-score are the metrics used to measure the performance of different machine learning models. Among these machine learning models SVM with GLCM as feature extraction technique using 10-fold cross validation gives the best classification result with 99.70% accuracy, 99.80% sensitivity and 97.03% F-score. We also ran these tests on different data sets and found that the results are similar across those too, as discussed later in the results section.
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Affiliation(s)
- Saroj Kumar Pandey
- Department of Computer Engineering & Applications, GLA University, Mathura, India
| | - Rekh Ram Janghel
- Department of Information Technology, National Institute of Information Technology, Raipur, India
| | | | - Rachana Kaabra
- Department of Information Technology, RCET, Bhilai, India
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12
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Attallah O. An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques. BIOSENSORS 2022; 12:bios12050299. [PMID: 35624600 PMCID: PMC9138764 DOI: 10.3390/bios12050299] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/06/2022] [Accepted: 04/24/2022] [Indexed: 06/01/2023]
Abstract
Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt
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13
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Fuadah YN, Lim KM. Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning. Front Physiol 2022; 12:761013. [PMID: 35185594 PMCID: PMC8850703 DOI: 10.3389/fphys.2021.761013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/29/2021] [Indexed: 11/13/2022] Open
Abstract
Cardiovascular disorders, including atrial fibrillation (AF) and congestive heart failure (CHF), are the significant causes of mortality worldwide. The diagnosis of cardiovascular disorders is heavily reliant on ECG signals. Therefore, extracting significant features from ECG signals is the most challenging aspect of representing each condition of ECG signal. Earlier studies have claimed that the Hjorth descriptor is assigned as a simple feature extraction algorithm capable of class separation among AF, CHF, and normal sinus rhythm (NSR) conditions. However, due to noise interference, certain features do not represent the characteristics of the ECG signals. This study addressed this critical gap by applying the discrete wavelet transform (DWT) to decompose the ECG signals into sub-bands and extracting Hjorth descriptor features and entropy-based features in the DWT domain. Therefore, the calculation of Hjorth descriptor and entropy-based features performed on each sub-band will produce more detailed information of ECG signals. The optimization of various classifier algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and radial basis function network (RBFN), was investigated to provide the best system performance. This study obtained an accuracy of 100% for the k-NN, SVM, RF, and ANN classifiers, respectively, and 97% for the RBFN classifier. The results demonstrated that the optimization of the classifier algorithm could improve the classification accuracy of AF, CHF, and NSR conditions, compared to earlier studies.
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Affiliation(s)
- Yunendah Nur Fuadah
- Computationa Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
| | - Ki Moo Lim
- Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
- *Correspondence: Ki Moo Lim,
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Sraitih M, Jabrane Y, Hajjam El Hassani A. An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques. J Clin Med 2021; 10:jcm10225450. [PMID: 34830732 PMCID: PMC8618527 DOI: 10.3390/jcm10225450] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 12/29/2022] Open
Abstract
The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients.
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Affiliation(s)
- Mohamed Sraitih
- MSC Laboratory, Cadi Ayyad University, Marrakech 40000, Morocco;
| | - Younes Jabrane
- MSC Laboratory, Cadi Ayyad University, Marrakech 40000, Morocco;
- Correspondence: ; Tel.: +212-524-434-745
| | - Amir Hajjam El Hassani
- Nanomedicine Imagery & Therapeutics Laboratory, EA4662—UBFC, UTBM, 90000 Belfort, France;
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15
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Su T, Liu C, Mu J, Zhou R, Jiang J. Geographical discrimination of ethanol based on stable isotope ratio analysis coupled with statistical methods: The Chinese case study. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 223:112604. [PMID: 34390986 DOI: 10.1016/j.ecoenv.2021.112604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 07/31/2021] [Accepted: 08/03/2021] [Indexed: 06/13/2023]
Abstract
The demand for the effective traceability of hazardous chemicals is crucial for preventing and controlling chemical spills and other accidents involving hazardous chemicals. The aim of the study was to investigate the correlation between the geographical location of ethanol-producing industrial sites and the carbon, hydrogen, and oxygen stable isotope ratios of the Chinese-manufactured ethanol using statistical classification analysis to enable the traceability of the ethanol. The isotopic data of 54 ethanol samples obtained from 18 different ethanol manufacturing plants in China between 2019 and 2020. The results of the statistical analysis demonstrated that the δ18O values of the ethanol positively correlated with latitudes of the production plants but negatively correlated with the δ13C values of the ethanol. A small number of samples derived from sites that were geographically close to each other could not be visually distinguished by PCA and HCA. However, by applying and comparing the results of classification by LDA, K-NN and Ensemble, an optimal classification model was obtained. Upon application of these models, 96.3% of the ethanol samples were correctly classified based on their geographical origin, indicating that the combination of isotopic ratios and latitude data is practical and effective for measuring the traceability of ethanol.
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Affiliation(s)
- Teng Su
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, PR China
| | - Chang Liu
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, PR China
| | - Jingjing Mu
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, PR China
| | - Ru Zhou
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, PR China.
| | - Juncheng Jiang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, PR China; School of Environment & Safety Engineering, Changzhou University, Changzhou 213164, PR China
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Mohammadi F, Sheikhani A, Razzazi F, Ghorbani Sharif A. Non-invasive localization of the ectopic foci of focal atrial tachycardia by using ECG signal based sparse decomposition algorithm. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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17
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Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier. Sci Rep 2021; 11:15092. [PMID: 34301998 PMCID: PMC8302656 DOI: 10.1038/s41598-021-94363-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 07/05/2021] [Indexed: 11/08/2022] Open
Abstract
Every human being has a different electro-cardio-graphy (ECG) waveform that provides information about the well being of a human heart. Therefore, ECG waveform can be used as an effective identification measure in biometrics and many such applications of human identification. To achieve fast and accurate identification of human beings using ECG signals, a novel robust approach has been introduced here. The databases of ECG utilized during the experimentation are MLII, UCI repository arrhythmia and PTBDB databases. All these databases are imbalanced; hence, resampling techniques are helpful in making the databases balanced. Noise removal is performed with discrete wavelet transform (DWT) and features are obtained with multi-cumulants. This approach is mainly based on features extracted from the ECG data in terms of multi-cumulants. The multi-cumulants feature based ECG data is classified using kernel extreme learning machine (KELM). The parameters of multi-cumulants and KELM are optimized using genetic algorithm (GA). Excellent classification rate is achieved with 100% accuracy on MLII and UCI repository arrhythmia databases, and 99.57% on PTBDB database. Comparison with existing state-of-art approaches has also been performed to prove the efficacy of the proposed approach. Here, the process of classification in the proposed approach is named as evolutionary hybrid classifier.
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Pandey SK, Janghel RR, Dev AV, Mishra PK. Automated arrhythmia detection from electrocardiogram signal using stacked restricted Boltzmann machine model. SN APPLIED SCIENCES 2021. [DOI: 10.1007/s42452-021-04621-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
AbstractSignificant advances in deep learning techniques have made it possible to offer technologically advanced methods to detect cardiac abnormalities. In this study, we have proposed a new deep learning based Restricted Boltzmann machine (RBM) model for the classification of arrhythmias from Electrocardiogram (ECG) signal. The work is divided into three phases where, in the first phase, signal processing is performed, including the normalization of the heartbeats as well as the segmentation of the heartbeats. In the second phase, the stacked RBM model is implemented which extracts the essential features from the ECG signal. Finally, a SoftMax activation function is used that classifies the ECG signal into four types of heartbeat classes according to ANSI/AAMA standards. This stacked RBM model is offered as three types of experiments, patient independent data classification for multi-class, patient independent data for binary classification, and patient specific classification. The best result was obtained using patient independent binary classification with an overall accuracy of 99.61%. For Patient Independent Multi Class classification, accuracy obtained was 98.61% and for patient specific data, the accuracy was 95.13%. The experimental results shows that the developed RBM model has better performance in terms of accuracy, sensitivity and specificity as compared to work mentioned in the other research papers.Article highlights
The proposed RBM model is skilled to automatically classify ECG heartbeat according to the ANSI- AAMI standards with accuracy, Recall, specificity.
The performance of the RBM model to correctly classify heartbeat classes was found to be improved.
The model is fully automatic, hence there is no requirement of additional system like feature extraction, feature selection, and classification.
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Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6630643. [PMID: 34055274 PMCID: PMC8112932 DOI: 10.1155/2021/6630643] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/01/2021] [Accepted: 04/01/2021] [Indexed: 11/18/2022]
Abstract
Automatic classification of ECG is very important for early prevention and auxiliary diagnosis of cardiovascular disease patients. In recent years, many studies based on ECG have achieved good results, most of which are based on single-label problems; one record corresponds to one label. However, in actual clinical applications, an ECG record may contain multiple diseases at the same time. Therefore, it is very important to study the multilabel ECG classification. In this paper, a multiscale residual deep neural network CSA-MResNet model based on the channel spatial attention mechanism is proposed. Firstly, the residual network is integrated into a multiscale manner to obtain the characteristics of ECG data at different scales and then increase the channel spatial attention mechanism to better focus on more important channels and more important ECG data fragments. Finally, the model is used to classify multilabel in large databases. The experimental results on the multilabel CCDD show that the CSA-MResNet model has an average F1 score of 88.2% when the multilabel classification of 9 ECGs is performed. Compared with the benchmark model, the F1 score of CSA-MResNet in the multilabel ECG classification increased by up to 1.7%. And, in the model verification on another database HF-challenge, the final average F1 score is 85.8%. Compared with the state-of-the-art methods, CSA-MResNet can help cardiologists perform early-stage rapid screening of ECG and has a certain generalization performance, providing a feasible analysis method for multilabel ECG classification.
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Pandey SK, Janghel RR. Classification of electrocardiogram signal using an ensemble of deep learning models. DATA TECHNOLOGIES AND APPLICATIONS 2021. [DOI: 10.1108/dta-05-2020-0108] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeAccording to the World Health Organization, arrhythmia is one of the primary causes of deaths across the globe. In order to reduce mortality rate, cardiovascular disease should be properly identified and the proper treatment for the same should be immediately provided to the patients. The objective of this paper was to implement a better heartbeat classification model which will work better than the other implemented heartbeat classification methods.Design/methodology/approachIn this paper, the ensemble of two deep learning models is proposed to classify the MIT-BIH arrhythmia database into four different classes according to ANSI-AAMI standards. First, a convolutional neural network (CNN) model is used to classify heartbeats on a raw data set. Secondly, four features (wavelets, R-R intervals, morphological and higher-order statistics) are extracted from the data set and then applied to a long short-term memory (LSTM) model to classify the heartbeats. Finally, the ensemble of CNN and LSTM model with sum rule, product rule and majority voting has been used to identify the heartbeat classes.FindingsAmong these, the highest accuracy obtained is 98.58% using ensemble method with product rule. The results show that the ensemble of CNN and BLSTM has offered satisfactory performance compared to other techniques discussed in this study.Originality/valueIn this study, we have developed a new combination of two deep learning models to enhance the performance of arrhythmia classification using segmentation of input ECG signals. The contributions of this study are as follows: First, a deep CNN model is built to classify ECG heartbeat using a raw data set. Second, four types of features (R-R interval, HOS, morphological and wavelet) were extracted from the raw data set and then applied to the bidirectional LSTM model to classify the ECG heartbeat. Third, combination rules (sum rules, product rules and majority voting rules) were tested to ensure the accumulated probabilities of the CNN and LSTM models.
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21
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Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8811837. [PMID: 33575022 PMCID: PMC7861929 DOI: 10.1155/2021/8811837] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 12/09/2020] [Accepted: 01/15/2021] [Indexed: 11/18/2022]
Abstract
Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules. In the preprocessing module, the signal is denoised and segmented. Then, multiple different features are extracted based on single heartbeat morphology, P length, QRS length, T length, PR interval, ST segment, QT interval, RR interval, R amplitude, and T amplitude. Subsequently, the features are combined and normalized, and the effect of different feature combinations on heartbeat classification is analyzed to select the optimal feature combination. Finally, the four types of normal and abnormal heartbeats were identified using the Stacking-DWKNN algorithm. This method is performed on the MIT-BIH arrhythmia database. The result shows a sensitivity of 89.42% and a positive predictive value of 94.90% of S-type beats and a sensitivity of 97.21% and a positive predictive value of 97.07% of V-type beats. The obtained average accuracy is 99.01%. Compared to other models with the same features, this method can improve accuracy and has a higher positive predictive value and sensitivity, which is important for clinical decision-making.
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